Abstract: A computer system displays a representation of a field of view of the one or more cameras, including a representation of a portion of a three-dimensional physical environment that is in the field of view of the one or more cameras. The computer system receives a request to add a first virtual effect to the displayed representation of the field of view of the one or more cameras. In response to receiving the request to add the first virtual effect to the displayed representation of the field of view of the one or more cameras and in accordance with a determination that the first virtual effect requires a scan of the physical environment, the computer system initiates a scan of the physical environment to detect one or more features of the physical environment and displays a user interface that indicates a progress of the scan of the physical environment.

Abstract: Systems, methods, apparatuses and non-transitory, computer-readable storage mediums are disclosed for generating AR self-portraits or “AR selfies.” In an embodiment, a method comprises: capturing, by a first camera of a mobile device, live image data, the live image data including an image of a subject in a physical, real-world environment; receiving, by a depth sensor of the mobile device, depth data indicating a distance of the subject from the camera in the physical, real-world environment; receiving, by one or more motion sensors of the mobile device, motion data indicating at least an orientation of the first camera in the physical, real-world environment; generating a virtual camera transform based on the motion data, the camera transform for determining an orientation of a virtual camera in a virtual environment; and generating a composite image data, using the image data, a matte and virtual background content selected based on the virtual camera orientation.

Abstract: A computer system displays a representation of a field of view of the one or more cameras, including a representation of a portion of a three-dimensional physical environment that is in the field of view of the one or more cameras. The computer system receives a request to add a first virtual effect to the displayed representation of the field of view of the one or more cameras. In response to receiving the request to add the first virtual effect to the displayed representation of the field of view of the one or more cameras and in accordance with a determination that the first virtual effect requires a scan of the physical environment, the computer system initiates a scan of the physical environment to detect one or more features of the physical environment and displays a user interface that indicates a progress of the scan of the physical environment.

Abstract: Systems, methods, apparatuses and non-transitory, computer-readable storage mediums are disclosed for generating AR self-portraits or “AR selfies.” In an embodiment, a method comprises: capturing, by a first camera of a mobile device, live image data, the live image data including an image of a subject in a physical, real-world environment; receiving, by a depth sensor of the mobile device, depth data indicating a distance of the subject from the camera in the physical, real-world environment; receiving, by one or more motion sensors of the mobile device, motion data indicating at least an orientation of the first camera in the physical, real-world environment; generating a virtual camera transform based on the motion data, the camera transform for determining an orientation of a virtual camera in a virtual environment; and generating a composite image data, using the image data, a matte and virtual background content selected based on the virtual camera orientation.

Abstract: Artistic styles extracted from one or more source images may be applied to one or more target images, e.g., in the form of stylized images and/or stylized video sequences. The extracted artistic style may be stored as a plurality of layers in a neural network, which neural network may be further optimized, e.g., via the fusion of various elements of the network's architectures. An optimized network architecture may be determined for each processing environment in which the network will be applied. The artistic style may be applied to the obtained images and/or video sequence of images using various optimization methods, such as the use of scalars to control the resolution of the unstylized and stylized images, temporal consistency constraints, as well as the use of dynamically adjustable or selectable versions of Deep Neural Networks (DNN) that are responsive to system performance parameters, such as available processing resources and thermal capacity.

Abstract: Systems, methods, apparatuses and non-transitory, computer-readable storage mediums are disclosed for generating AR self-portraits or “AR selfies.” In an embodiment, a method comprises: capturing, by a first camera of a mobile device, image data, the image data including an image of a subject in a physical, real-world environment; receiving, by a depth sensor of the mobile device, depth data indicating a distance of the subject from the camera in the physical, real-world environment; receiving, by one or more motion sensors of the mobile device, motion data indicating at least an orientation of the first camera in the physical, real-world environment; generating a virtual camera transform based on the motion data, the camera transform for determining an orientation of a virtual camera in a virtual environment; and generating a composite image data, using the image data, a matte and virtual background content selected based on the virtual camera orientation.

Abstract: Systems, methods, apparatuses and non-transitory, computer-readable storage mediums are disclosed for generating AR self-portraits or “AR selfies.” In an embodiment, a method comprises: capturing, by a first camera of a mobile device, image data, the image data including an image of a subject in a physical, real-world environment; receiving, by a depth sensor of the mobile device, depth data indicating a distance of the subject from the camera in the physical, real-world environment; receiving, by one or more motion sensors of the mobile device, motion data indicating at least an orientation of the first camera in the physical, real-world environment; generating a virtual camera transform based on the motion data, the camera transform for determining an orientation of a virtual camera in a virtual environment; and generating a composite image data, using the image data, a matte and virtual background content selected based on the virtual camera orientation.

Abstract: Systems, methods, apparatuses and non-transitory, computer-readable storage mediums are disclosed for generating AR self-portraits or “AR selfies.” In an embodiment, a method comprises: capturing, by a first camera of a mobile device, live image data, the live image data including an image of a subject in a physical, real-world environment; receiving, by a depth sensor of the mobile device, depth data indicating a distance of the subject from the camera in the physical, real-world environment; receiving, by one or more motion sensors of the mobile device, motion data indicating at least an orientation of the first camera in the physical, real-world environment; generating a virtual camera transform based on the motion data, the camera transform for determining an orientation of a virtual camera in a virtual environment; and generating a composite image data, using the image data, a matte and virtual background content selected based on the virtual camera orientation.